Malakar Sukanta
Centre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302 India.
Model Earth Syst Environ. 2022;8(3):3103-3116. doi: 10.1007/s40808-021-01287-1. Epub 2021 Sep 27.
COVID-19 is a worldwide transmitted pandemic that has brought a threatening challenge to Indian society and the economy. The disease has become a public health disaster, which has no effective medication. However, proper management and planning, which includes understanding the transmitting pattern, number of containment zones, vulnerable factors, and level of risk, may break the chain of transmission and reduce the number of cases. Hence, this study has attempted to model the COVID-19 vulnerability using an integrated fuzzy multi-criteria decision-making (MCDM) approach, namely fuzzy-analytical hierarchy process (AHP) and fuzzy-technique for order preference by similarity to ideal solution (TOPSIS) for West Bengal, India, through geographic information system (GIS). A total of 15 parameters were utilised to model the COVID-19 vulnerability, which was further categorised into three criteria: social vulnerability, epidemiological vulnerability, and physical vulnerability. The final vulnerability mapping has been done using these three criteria through the GIS platform. This study reveals that COVID-19 infection highly threatens about 20% of the total area of West Bengal, 23.42% moderately vulnerable, and 57.03% of the area comes under low vulnerability. The highly vulnerable region includes the Kolkata, South 24 Paraganas, and North 24 Paraganas, which are considered highly populated districts of West Bengal. Therefore government agencies should be more focused and plan accordingly to safeguard the community, especially the region with very high COVID-19 vulnerability, from further spreading the infection.
新冠疫情是一场全球传播的大流行病，给印度社会和经济带来了严峻挑战。该疾病已演变成一场公共卫生灾难，目前尚无有效药物。然而，通过了解传播模式、管控区域数量、脆弱因素和风险水平等进行合理的管理和规划，或许能够打破传播链并减少病例数量。因此，本研究试图运用一种综合模糊多准则决策（MCDM）方法，即模糊层次分析法（AHP）和与理想解相似的序贯偏好技术（TOPSIS），通过地理信息系统（GIS）对印度西孟加拉邦的新冠疫情脆弱性进行建模。总共使用了15个参数来构建新冠疫情脆弱性模型，这些参数进一步被归类为三个准则：社会脆弱性、流行病学脆弱性和物理脆弱性。最终的脆弱性地图是通过GIS平台利用这三个准则绘制而成的。本研究表明，新冠疫情感染对西孟加拉邦约20%的总面积构成高度威胁，23.42%为中度脆弱，57.03%的区域属于低脆弱性。高脆弱性地区包括加尔各答、南24区和北24区，这些都是西孟加拉邦人口密集的地区。因此，政府机构应更加关注并据此制定计划，以保护社区，特别是新冠疫情高脆弱性地区，防止感染进一步蔓延。
